According to the 2010 census data report, 19.2 million Americans suffer from some form of disability and are unable to perform typical activities of daily living (ADL) independently or without the aid of a human helper. Many of these people have tetraplegia or are partially paralytic. Some suffer from a brainstem stroke or amyotrophic lateral sclerosis (ALS). In many cases, the person eventually becomes completely locked-in and is unable to move any muscle in his or her body.
A brain-computer interface (BCI) driving a robotic system can enable such persons to carry out their ADLs by simply focusing their attention on a screen displaying a task workspace and the stimuli for an activity they can choose. This generates signals in the brain that can be analyzed and processed by a BCI system. As a result, the BCI system can detect human intention and command a robot to execute a task without the need for the human to move any muscles. In one such system, discrete movements of a robotic arm can be controlled using such brain signals. Unfortunately, the process of moving the arm using a BCI system, for example, to grasp an object, can be quite slow. For example, it may take 20-30 seconds to detect each user command using a BCI system and many such commands may be necessary to manipulate the arm as needed. In addition to being inefficient, this process requires great effort from the user and can cause undue fatigue.
From the above discussion it can be appreciated that it would be desirable to have a BCI system that requires fewer user commands to perform a given action.